Extreme weather events such as winter snowstorms and cold air outbreaks (CAO) pose great threats to human life and to the socioeconomic well being of modern society. Such extreme weather events have caused school/business closure, costly road/highway snow removal and deicing, loss of agricultural products and mass flight cancellations. Accurate forecasts of high impact weather at a lead time of one month or longer are needed for business planners, decision makers at all levels of local, state, and federal government agencies, and energy producers/consumers and energy traders. Extreme weather events such as CAOs create a temporary (which can last several weeks) supply-demand imbalance or high-demand period for commodities such as natural gas and electricity. Despite 50 years of history of numerical weather predictions, the existing numerical weather prediction models still cannot provide useful information about individual winter storms and their associated CAOs at a lead time beyond two weeks. This two-week predictability limit of numerical weather predictions is due to the inherent nonlinear dynamics constraints characterized by the chaotic nature of the atmospheric motions, or the so-called “butterfly effect”. Such a limit severely compromises the ability to plan ahead and devise an optimal strategy to minimize the adverse impacts of winter snowstorms and CAOs.
Weather forecasts having a lead time of 14-60 days (referred to as the sub-seasonal range) are regarded as the most challenging problem in the business of weather forecasts. Sub-seasonal forecasts issued by most operational forecast centers, such as Climate Prediction Center (CPC) of the U.S. National Weather Service and the UK Met Office, are made by a combination of empirical and dynamical prediction tools. Dynamical tools derive forecasts by integrating global atmospheric models with prescribed ocean surface temperature or coupled ocean-atmosphere global models. Currently, the dynamical forecasts used by CPC for sub-seasonal forecasts are derived from the Climate Forecast System Version 2 (CFSv2) model. The CFSv2 forecasts are made 4 times a day. Each forecast is made by integrating the CFSv2 model starting from the observed states of the ocean, land surface, and atmosphere at the initial time and ending in 9 months later. There are 3 additional perturbed runs at 0 UTC (Universal Time Coordinated) out to one season and 3 additional perturbed runs at 6, 12 and 18 UTC out to 45 days. Despite the fact that CFSv2 model outputs are available at least once per day from the initial time to 9 months after, forecasters rarely use the information derived from daily forecast outputs to predict weather at a lead time of longer than 2 weeks when the skill of forecasts for individual weather events degrades significantly. The only advisory information provided to stakeholders is the monthly/seasonal average temperature and rainfall anomalies, without concerning the timing information of individual weather events.
Accordingly, what is needed in the art is a system and method for predicting extreme weather events which allows for a lead time of greater than two weeks prior to the event.
The prior art empirical models have been derived purely from statistics analysis that either empirically predict dominant climate variability modes or relate particular phases of these modes to monthly and/or seasonal mean anomalies via their statistical relationships. The empirical models used in CPC include canonical correlation analysis, optimum climate normal, regression tool, and ensemble canonical correlation analysis. One of CPC's empirical forecast tools, used only during ENSO episodes, is the ENSO composites, representing historical teleconnections over the United States for El Nino or La Nina years. Another empirical forecast tool is Maddan-Julian Oscillation (MJO) composites. A hybrid paradigm using the phase information of ENSO/MJO phase model has been developed and put into practice in the CPC's experimental week 3-4 outlook. Prediction skills for the map of surface air temperature are improved by conditioned warm and cold phases of ENSO and strong MJO, but for periods without large sea surface temperature anomalies (e.g., non ENSO years, weak and transition phases of MJO), large internal variability makes the forecasts of the “forced” anomalies indecisive.
Other dominant recurring patterns in the tropospheric extratropics, such as the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the Pacific North-American Oscillation (PNA), which are closely related with the surface weather regimes, cannot be predicted at a lead time from sub-seasonal to seasonal (S2S) range, though the prediction skills at seasonal and longer time scales have been significantly improved in models with prescribed ocean surface temperature or in coupled ocean-atmosphere global models and thus the predictability of those patterns can only help improve seasonal forecasts of mean surface temperature.
Stratospheric circulation anomalies and tropical forcing have been found to be promising precursors to extreme cold events in mid-latitudes. Extreme cold events in eastern North America, northern Europe, and eastern Asia tend to take place more frequently in the period of 1-2 months after a weaker stratospheric polar vortex. It has also been found that cold temperature anomalies tend to occur over the southeastern United States in 1-2 weeks after the peak days of weak vortex events, whereas cold anomalies occur over Eurasia at the inception of weak vortex events. The temperature anomaly patterns associated with different types of extreme weak polar vortex events have been further shown and it has been reported that cold temperature anomalies tend to take place over the regions underneath the intrusion of the stratospheric air into the troposphere (or tropopause foldings). The easterly phase of the Quasi-Biannual-Oscillation (QBO) in the equatorial stratosphere is favorable for below-normal winter mean temperature over most of the two continents. The warm phase of ENSO events favors cold anomalies in the southeastern United States whereas the MJO-associated convection located over the Indian Ocean favors more extreme cold air surge events in East Asia in winter.
The stratospheric connection to the AO and the associated extreme weather events have been recognized as a new opportunity for sub-seasonal climate predictions in winter seasons since the stratospheric signal provides a long lead information (0-60 days) to anomalous surface weather regimes. With more satellite data in the stratosphere to assimilate and increased vertical resolution, the capability is now available to use numerical weather prediction (NWP) models as an initial value problem for stratospheric forecasts in the extratropics. A useful skill in the sub-seasonal range has been found in predicting extreme polar vortex events and zonal mean temperature, geopotential height, and wind anomalies. The findings of these studies indicate that the useful skill for sub-seasonal forecasts in extratropical stratosphere is due to the models' ability to retain amplitude of planetary waves throughout the sub-seasonal range, despite that the models may not be able to predict the exact locations of planetary waves and their spatial scales beyond the 2-week range.
The conventional wisdom is that the stratosphere is more predictable than the troposphere because of its longer persistence time scale from the dominance of the quasi-stationary planetary scale Rossby waves over the fast moving synoptic scale waves. However, the long persistency does not necessarily imply that a dynamical model would have to have higher prediction skill in both absolute term and in reference to persistent forecasts, although it may yield a higher prediction skill for persistent forecasts. For example, numerical model forecasts in spring seasons have a higher prediction skill in the absolute term for polar stratospheric anomalies than that for equatorial stratospheric anomalies, although the latter has much longer persistent time scale than the former. In reference to persistent forecasts, operational models are much more skillful over the extratropical stratosphere than over the equatorial stratosphere. In this sense, the prediction skill of a dynamical model is not always related to the persistency. Furthermore, it has been shown that for stratospheric sudden warming events and for everyday cases, a numerical model actually has a higher skill in predicting zonal mean flow anomalies than quasi-stationary wave anomalies. Therefore, the lack of synoptic-scale waves in the extratropical stratosphere, which contributes to the relatively long persistency in the extratropical stratosphere in comparison with the troposphere below, is not the most essential factor why a numerical model would have a higher skill there. In addition, the longer radiative cooling time scale in the stratosphere could help to explain why it would be easier to predict the recovery of stratospheric polar vortex. However, it cannot explain why the onset of stratospheric warming events is equally predictable. These examples seem to support the conjecture that if a numerical model has good skill for stratospheric predictions beyond the inherent 2-week predictability limit for the troposphere, it cannot be just due to the relatively long persistence time scale alone.
Evidence has been provided that the prediction skill of tropospheric anomalies by operational models depends on the initial conditions in the stratosphere. Model forecasts for monthly mean tropospheric anomalies have significantly greater skill in the months after stratospheric sudden warming events. As to sub-seasonal forecasts, it has been suggested that the skill improvement in the sub-seasonal range depends on the initial day of the forecast relative to the onset date of stratospheric sudden warming events. The skill dependency on the initial conditions in the stratosphere suggests that the ability of operational models to capture the coupling information between stratospheric and tropospheric anomalies allows the stratospheric variability with longer predictability limit to enhance the forecast skills of troposphere at the sub-seasonal range. However, such skill improvement at the sub-seasonal range for troposphere is not significant in the absence of large amplitude stratospheric anomalies.
As reported in the literature, many stratospheric circulation indices, such as the stratospheric northern annular mode (NAM), zonal mean zonal wind surrounding the polar vortex, polar vortex oscillation, eddy poleward heat flux (or the vertical component of the Eliassen-Palm flux), and stream function of the residual circulation inferred from the downward control principle, may bear long lead precursor information (0-60 days) for surface temperature anomalies. Such long lead information has been utilized in statistical models for predicting monthly or seasonal mean surface temperature anomalies, but not for sub-seasonal forecasts of individual CAOs in a real time (or operational) setting yet.
The work of others in the field have documented the existence of the global meridional mass circulation system that connects the tropics to the poles and the troposphere to the stratosphere via the poleward warm air branch in the upper troposphere (as well as the winter hemisphere's stratosphere) and the equatorward cold branch in the lower troposphere. It has been postulated that the poleward propagating positive stratospheric temperature anomalies are associated with a stronger warm air branch of the meridional mass circulation, and vice versa. The negative phase of stratospheric NAM events is associated with a stronger poleward mass circulation whereas the positive phase is associated with a weaker circulation. Because the poleward propagation in the upper stratosphere tends to be ahead of that in the lower stratosphere, there exists a downward propagation of stratospheric anomalies of both signs associated with stratospheric NAM events.
Recently, two climatological mean routes or main streams of cold air from the polar region to lower latitudes within the cold air branch of the meridional mass circulation have been identified, namely the “East Asian stream” and the “North American stream”. The “East Asian stream” intensifies over the northern part of Eurasia, while flowing eastward. It then turns southeastward toward East Asia via Siberia and dissipates over the western North Pacific Ocean. The “North American stream” intensifies over the Arctic Ocean and moves toward the East Coast of North America via Hudson Bay and dissipates over the western North Atlantic Ocean. A comprehensive isentropic diagnosis of East Asian CAOs within the cold air branch of the meridional mass circulation has been previously conducted. It has been shown that the timings of CAOs in mid-latitudes are associated with the strengthening of equatorward cold air mass transport in the lower troposphere and the latter is nearly synchronized with the poleward warm air mass transport in the upper atmosphere (including the stratosphere) into the polar region. These results show that there exist two dominant geographical patterns of temperature anomalies, which tend to occur during the cold air discharge period (or 1-10 days after a stronger mass circulation across the polar circle). One represents cold anomalies mainly in the midlatitudes of both North America and Eurasia, and the other represents cold anomalies mainly over only one of the two continents accompanied with abnormal warmth over the other continent. The existence of such robust relationship between CAO events and the meridional mass transport in upper levels is because the variability of meridional mass transport of cold and warm air and corresponding variability of surface weather systems that are responsible for CAOs is linked to the strengthening/weakening of the poleward warm air mass transport in the upper atmosphere. They further show that the meridional mass circulation in the stratosphere, which is part of the poleward warm air branch, tends to be in phase with the total poleward warm air mass transport above the upper troposphere, with a time lag of few days. This indicates that the poleward mass transport into the polar region in both the upper troposphere and the stratosphere is linked to the cold air activities near surface in boreal winter. In addition, the variability of the annular modes (e.g., NAM and AO), which are directly related with the surface weather regime, can be physically explained and numerically accounted for from the atmospheric mass circulation variation.
The mechanism for the simultaneous poleward mass flux in upper isentropic layers and equatorward mass flux below in the extratropics has been uniquely attributed to the dominance of westward tilted baroclinic waves. Due to hydrostatic and (quasi) geostrophic balance, westward tilted baroclinic waves always have a net poleward mass transport in upper levels and equatorward mass transport in lower levels. Intensification of westward tilted waves results in a near-simultaneous increase in both the poleward mass transport into the polar region aloft and the equatorward discharge of the cold polar air mass in the lower troposphere, responsible for CAOs in mid-latitudes. Deep and large-amplitude baroclinic waves are capable of driving a strong meridional mass circulation that is connected to the stratosphere. The extra air mass brought to the polar region by a stronger poleward mass circulation contributes to a rising of the surface pressure over the Arctic before the cold air in the lower polar troposphere is carried out by a stronger equatorward cold air branch. This explains why the AO or tropospheric NAM tends to be nearly in phase with the lower stratospheric NAM. Therefore, the stronger poleward mass transport in the extratropical stratosphere can be a robust indicator of the equatorward mass transport out of the polar region in the lower troposphere. This is the basis for the existence of the physical causal relationship between the mass circulation in the stratosphere and CAOs.